Introduction_to_MAchine_Learning_Advance.pptx

Introduction to Machine Learning

1. INTRODUCTION TO Machine Learning 3rd Edition ETHEM ALPAYDIN © The MIT Press, 2014 [email protected] http://www.cmpe.boun.edu.tr/~ ethem/i2ml3e Lecture Slides for

2. CHAPTER 1: Introduction

3. Big Data 3 The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

4. Big Data 4 Widespread use of personal computers and wireless communication we all became producers of data that leads to “big data” Every time we buy a product, every time we rent a movie, visit a web page, write a blog, or post on the social media, even when we just walk or drive around, we are generating data . We are both producers and consumers of data (Products and Services ) Think, for example, of a supermarket that is selling thousands of goods to millions of customers all over a country or through a virtual store over the web. What the supermarket wants is to be able to predict which customer is likely to buy which product , to maximize sales and profit. Similarly each customer wants to find the set of products best matching his/her needs. Customer behavior changes in time and by geographic location, Data is not random, it has structure (Pattern) , e.g., customer behavior We need “big theory” to extract that structure from data for (a) Understanding the process (b) Making predictions for the future

5. Why “Learn” ? 5 Machine learning is a growing technology which enables computers to learn automatically from past data or past experience. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently , it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more . There is no need to “learn” to calculate payroll Learning is used when: Human expertise does not exist (navigating on Mars), Humans are unable to explain their expertise (speech recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases (user biometrics)

6. What We Talk About When We Talk About “ Learning” 6 Learning general models from a data of particular examples Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce. Example in retail: Customer transactions to consumer behavior: People who bought “Blink” also bought “Outliers” (www.amazon.com) Build a model that is a good and useful approximation to the data.

7. Data Mining 7 Retail: Market basket analysis, Customer relationship management (CRM) Finance: Credit scoring, fraud detection Manufacturing: Control, robotics, troubleshooting Medicine: Medical diagnosis Telecommunications: Spam filters, intrusion detection Bioinformatics: Motifs, alignment Web mining: Search engines ...

8. What is Machine Learning? 8 Optimize a performance criterion using example data or past experience. Role of Statistics: Inference from a sample Role of Computer science: Efficient algorithms to Solve the optimization problem Representing and evaluating the model for inference

9. Applications 9 Association Supervised Learning Classification Regression Unsupervised Learning Reinforcement Learning

10. Learning Associations 10 Basket analysis: P ( Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | beer ) = 0.7

11. Classification 11 Example: Credit scoring Differentiating between low-risk and high-risk customers from their income and savings Discriminant: IF income > θ 1 AND savings > θ 2 THEN low-risk ELSE high-risk

12. Classification: Applications 12 Aka Pattern recognition Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Character recognition: Different handwriting styles. Speech recognition: Temporal dependency. Medical diagnosis: From symptoms to illnesses Biometrics: Recognition/authentication using physical and/or behavioral characteristics: Face, iris, signature, etc Outlier/novelty detection:

13. Face Recognition 13 Training examples of a person Test images ORL dataset, AT&T Laboratories, Cambridge UK

14. Regression Example: Price of a used car x : car attributes y : price y = g ( x | q ) g ( ) model, q parameters 14 y = wx + w

15. Regression Applications 15 Navigating a car: Angle of the steering Kinematics of a robot arm α 1 = g 1 ( x , y ) α 2 = g 2 ( x , y ) α 1 α 2 ( x , y ) Response surface design

16. Supervised Learning: Uses 16 Prediction of future cases: Use the rule to predict the output for future inputs Knowledge extraction: The rule is easy to understand Compression: The rule is simpler than the data it explains Outlier detection: Exceptions that are not covered by the rule, e.g., fraud

17. Unsupervised Learning 17 Learning “what normally happens” No output Clustering: Grouping similar instances Example applications Customer segmentation in CRM Image compression: Color quantization Bioinformatics: Learning motifs

18. Reinforcement Learning 18 Learning a policy: A sequence of outputs No supervised output but delayed reward Credit assignment problem Game playing Robot in a maze Multiple agents, partial observability, ...

19. Resources: Datasets 19 UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html Statlib : http://lib.stat.cmu.edu /

20. Resources: Journals 20 Journal of Machine Learning Research www.jmlr.org Machine Learning Neural Computation Neural Networks IEEE Trans on Neural Networks and Learning Systems IEEE Trans on Pattern Analysis and Machine Intelligence Journals on Statistics/Data Mining/Signal Processing/Natural Language Processing/Bioinformatics/...

21. Resources: Conferences 21 International Conference on Machine Learning (ICML) European Conference on Machine Learning (ECML) Neural Information Processing Systems (NIPS) Uncertainty in Artificial Intelligence (UAI) Computational Learning Theory (COLT) International Conference on Artificial Neural Networks (ICANN) International Conference on AI & Statistics (AISTATS) International Conference on Pattern Recognition (ICPR) ...